Face Generation

In this project, you'll use generative adversarial networks to generate new images of faces.

Get the Data

You'll be using two datasets in this project:

  • MNIST
  • CelebA

Since the celebA dataset is complex and you're doing GANs in a project for the first time, we want you to test your neural network on MNIST before CelebA. Running the GANs on MNIST will allow you to see how well your model trains sooner.

If you're using FloydHub, set data_dir to "/input" and use the FloydHub data ID "R5KrjnANiKVhLWAkpXhNBe".

In [1]:
data_dir = './data'

# FloydHub - Use with data ID "R5KrjnANiKVhLWAkpXhNBe"
#data_dir = '/input'


"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import helper

helper.download_extract('mnist', data_dir)
helper.download_extract('celeba', data_dir)
Found mnist Data
Found celeba Data

Explore the Data

MNIST

As you're aware, the MNIST dataset contains images of handwritten digits. You can view the first number of examples by changing show_n_images.

In [2]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
%matplotlib inline
import os
from glob import glob
from matplotlib import pyplot

mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'mnist/*.jpg'))[:show_n_images], 28, 28, 'L')
pyplot.imshow(helper.images_square_grid(mnist_images, 'L'), cmap='gray')
Out[2]:
<matplotlib.image.AxesImage at 0x7f5da1e13b00>

CelebA

The CelebFaces Attributes Dataset (CelebA) dataset contains over 200,000 celebrity images with annotations. Since you're going to be generating faces, you won't need the annotations. You can view the first number of examples by changing show_n_images.

In [3]:
show_n_images = 25

"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
mnist_images = helper.get_batch(glob(os.path.join(data_dir, 'img_align_celeba/*.jpg'))[:show_n_images], 28, 28, 'RGB')
pyplot.imshow(helper.images_square_grid(mnist_images, 'RGB'))
Out[3]:
<matplotlib.image.AxesImage at 0x7f5d9f5bacc0>

Preprocess the Data

Since the project's main focus is on building the GANs, we'll preprocess the data for you. The values of the MNIST and CelebA dataset will be in the range of -0.5 to 0.5 of 28x28 dimensional images. The CelebA images will be cropped to remove parts of the image that don't include a face, then resized down to 28x28.

The MNIST images are black and white images with a single color channel while the CelebA images have 3 color channels (RGB color channel).

Build the Neural Network

You'll build the components necessary to build a GANs by implementing the following functions below:

  • model_inputs
  • discriminator
  • generator
  • model_loss
  • model_opt
  • train

Check the Version of TensorFlow and Access to GPU

This will check to make sure you have the correct version of TensorFlow and access to a GPU

In [4]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
from distutils.version import LooseVersion
import warnings
import tensorflow as tf

# Check TensorFlow Version
assert LooseVersion(tf.__version__) >= LooseVersion('1.0'), 'Please use TensorFlow version 1.0 or newer.  You are using {}'.format(tf.__version__)
print('TensorFlow Version: {}'.format(tf.__version__))

# Check for a GPU
if not tf.test.gpu_device_name():
    warnings.warn('No GPU found. Please use a GPU to train your neural network.')
else:
    print('Default GPU Device: {}'.format(tf.test.gpu_device_name()))
TensorFlow Version: 1.2.1
Default GPU Device: /gpu:0

Input

Implement the model_inputs function to create TF Placeholders for the Neural Network. It should create the following placeholders:

  • Real input images placeholder with rank 4 using image_width, image_height, and image_channels.
  • Z input placeholder with rank 2 using z_dim.
  • Learning rate placeholder with rank 0.

Return the placeholders in the following the tuple (tensor of real input images, tensor of z data)

In [5]:
import problem_unittests as tests

def model_inputs(image_width, image_height, image_channels, z_dim):
    """
    Create the model inputs
    :param image_width: The input image width
    :param image_height: The input image height
    :param image_channels: The number of image channels
    :param z_dim: The dimension of Z
    :return: Tuple of (tensor of real input images, tensor of z data, learning rate)
    """
    # TODO: Implement Function
    input_real = tf.placeholder(tf.float32, (None, image_width, image_height, image_channels), name='input_real')
    input_z = tf.placeholder(tf.float32, (None, z_dim), name='input_z')
    learning_rate = tf.placeholder(tf.float32)
    return input_real, input_z, learning_rate


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_inputs(model_inputs)
ERROR:tensorflow:==================================
Object was never used (type <class 'tensorflow.python.framework.ops.Operation'>):
<tf.Operation 'assert_rank_2/Assert/Assert' type=Assert>
If you want to mark it as used call its "mark_used()" method.
It was originally created here:
['File "/home/carnd/anaconda3/envs/dl/lib/python3.6/runpy.py", line 193, in _run_module_as_main\n    "__main__", mod_spec)', 'File "/home/carnd/anaconda3/envs/dl/lib/python3.6/runpy.py", line 85, in _run_code\n    exec(code, run_globals)', 'File "/home/carnd/anaconda3/envs/dl/lib/python3.6/site-packages/ipykernel_launcher.py", line 16, in <module>\n    app.launch_new_instance()', 'File "/home/carnd/anaconda3/envs/dl/lib/python3.6/site-packages/traitlets/config/application.py", line 658, in launch_instance\n    app.start()', 'File "/home/carnd/anaconda3/envs/dl/lib/python3.6/site-packages/ipykernel/kernelapp.py", line 477, in start\n    ioloop.IOLoop.instance().start()', 'File "/home/carnd/anaconda3/envs/dl/lib/python3.6/site-packages/zmq/eventloop/ioloop.py", line 177, in start\n    super(ZMQIOLoop, self).start()', 'File "/home/carnd/anaconda3/envs/dl/lib/python3.6/site-packages/tornado/ioloop.py", line 888, in start\n    handler_func(fd_obj, events)', 'File "/home/carnd/anaconda3/envs/dl/lib/python3.6/site-packages/tornado/stack_context.py", line 277, in null_wrapper\n    return fn(*args, **kwargs)', 'File "/home/carnd/anaconda3/envs/dl/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events\n    self._handle_recv()', 'File "/home/carnd/anaconda3/envs/dl/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv\n    self._run_callback(callback, msg)', 'File "/home/carnd/anaconda3/envs/dl/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback\n    callback(*args, **kwargs)', 'File "/home/carnd/anaconda3/envs/dl/lib/python3.6/site-packages/tornado/stack_context.py", line 277, in null_wrapper\n    return fn(*args, **kwargs)', 'File "/home/carnd/anaconda3/envs/dl/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 283, in dispatcher\n    return self.dispatch_shell(stream, msg)', 'File "/home/carnd/anaconda3/envs/dl/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 235, in dispatch_shell\n    handler(stream, idents, msg)', 'File "/home/carnd/anaconda3/envs/dl/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 399, in execute_request\n    user_expressions, allow_stdin)', 'File "/home/carnd/anaconda3/envs/dl/lib/python3.6/site-packages/ipykernel/ipkernel.py", line 196, in do_execute\n    res = shell.run_cell(code, store_history=store_history, silent=silent)', 'File "/home/carnd/anaconda3/envs/dl/lib/python3.6/site-packages/ipykernel/zmqshell.py", line 533, in run_cell\n    return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)', 'File "/home/carnd/anaconda3/envs/dl/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2698, in run_cell\n    interactivity=interactivity, compiler=compiler, result=result)', 'File "/home/carnd/anaconda3/envs/dl/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2808, in run_ast_nodes\n    if self.run_code(code, result):', 'File "/home/carnd/anaconda3/envs/dl/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2862, in run_code\n    exec(code_obj, self.user_global_ns, self.user_ns)', 'File "<ipython-input-5-98416cbed9bf>", line 22, in <module>\n    tests.test_model_inputs(model_inputs)', 'File "/home/carnd/face_generation/problem_unittests.py", line 12, in func_wrapper\n    result = func(*args)', 'File "/home/carnd/face_generation/problem_unittests.py", line 68, in test_model_inputs\n    _check_input(learn_rate, [], \'Learning Rate\')', 'File "/home/carnd/face_generation/problem_unittests.py", line 34, in _check_input\n    _assert_tensor_shape(tensor, shape, \'Real Input\')', 'File "/home/carnd/face_generation/problem_unittests.py", line 20, in _assert_tensor_shape\n    assert tf.assert_rank(tensor, len(shape), message=\'{} has wrong rank\'.format(display_name))', 'File "/home/carnd/anaconda3/envs/dl/lib/python3.6/site-packages/tensorflow/python/ops/check_ops.py", line 617, in assert_rank\n    dynamic_condition, data, summarize)', 'File "/home/carnd/anaconda3/envs/dl/lib/python3.6/site-packages/tensorflow/python/ops/check_ops.py", line 571, in _assert_rank_condition\n    return control_flow_ops.Assert(condition, data, summarize=summarize)', 'File "/home/carnd/anaconda3/envs/dl/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py", line 170, in wrapped\n    return _add_should_use_warning(fn(*args, **kwargs))', 'File "/home/carnd/anaconda3/envs/dl/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py", line 139, in _add_should_use_warning\n    wrapped = TFShouldUseWarningWrapper(x)', 'File "/home/carnd/anaconda3/envs/dl/lib/python3.6/site-packages/tensorflow/python/util/tf_should_use.py", line 96, in __init__\n    stack = [s.strip() for s in traceback.format_stack()]']
==================================
Tests Passed

Discriminator

Implement discriminator to create a discriminator neural network that discriminates on images. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "discriminator" to allow the variables to be reused. The function should return a tuple of (tensor output of the discriminator, tensor logits of the discriminator).

In [6]:
def discriminator(images, reuse=False):
    """
    Create the discriminator network
    :param images: Tensor of input image(s)
    :param reuse: Boolean if the weights should be reused
    :return: Tuple of (tensor output of the discriminator, tensor logits of the discriminator)
    """
    # TODO: Implement Function
    with tf.variable_scope('discriminator', reuse=reuse):
        alpha = 0.1
        
        # Input layer is 28x28x?
        x1 = tf.layers.conv2d(images, 64, 5, strides=2, padding='same')
        relu1 = tf.maximum(alpha * x1, x1)
        # 14x14x64
        
        x2 = tf.layers.conv2d(relu1, 128, 5, strides=2, padding='same')
        bn2 = tf.layers.batch_normalization(x2, training=True)
        relu2 = tf.maximum(alpha * bn2, bn2)
        # 7x7x128
        
        x3 = tf.layers.conv2d(relu2, 256, 5, strides=2, padding='valid')
        bn3 = tf.layers.batch_normalization(x3, training=True)
        relu3 = tf.maximum(alpha * bn3, bn3)
        # 4x4x256

        # Flatten it
        flat = tf.reshape(relu3, (-1, 4*4*256))
        logits = tf.layers.dense(flat, 1)
        out = tf.sigmoid(logits)
    
    return out, logits


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_discriminator(discriminator, tf)
Tests Passed

Generator

Implement generator to generate an image using z. This function should be able to reuse the variables in the neural network. Use tf.variable_scope with a scope name of "generator" to allow the variables to be reused. The function should return the generated 28 x 28 x out_channel_dim images.

In [7]:
def generator(z, out_channel_dim, is_train=True):
    """
    Create the generator network
    :param z: Input z
    :param out_channel_dim: The number of channels in the output image
    :param is_train: Boolean if generator is being used for training
    :return: The tensor output of the generator
    """
    # TODO: Implement Function
    with tf.variable_scope('generator', reuse=not is_train):
        alpha = 0.1
        
        # First fully connected layer
        x1 = tf.layers.dense(z, 4*4*512)
        # Reshape it to start the convolutional stack
        x1 = tf.reshape(x1, (-1, 4, 4, 512))
        x1 = tf.layers.batch_normalization(x1, training=is_train)
        x1 = tf.maximum(alpha * x1, x1)
        # 4x4x512 now
        
        x2 = tf.layers.conv2d_transpose(x1, 256, 4, strides=1, padding='valid')
        x2 = tf.layers.batch_normalization(x2, training=is_train)
        x2 = tf.maximum(alpha * x2, x2)
        # 7x7x256 now
        
        x3 = tf.layers.conv2d_transpose(x2, 128, 5, strides=2, padding='same')
        x3 = tf.layers.batch_normalization(x3, training=is_train)
        x3 = tf.maximum(alpha * x3, x3)
        # 14x14x128 now
        
        # Output layer
        logits = tf.layers.conv2d_transpose(x3, out_channel_dim, 5, strides=2, padding='same')
        # 28x28x? now
        
        out = tf.tanh(logits)
        
    return out


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_generator(generator, tf)
Tests Passed

Loss

Implement model_loss to build the GANs for training and calculate the loss. The function should return a tuple of (discriminator loss, generator loss). Use the following functions you implemented:

  • discriminator(images, reuse=False)
  • generator(z, out_channel_dim, is_train=True)
In [8]:
def model_loss(input_real, input_z, out_channel_dim):
    """
    Get the loss for the discriminator and generator
    :param input_real: Images from the real dataset
    :param input_z: Z input
    :param out_channel_dim: The number of channels in the output image
    :return: A tuple of (discriminator loss, generator loss)
    """
    # TODO: Implement Function
    g_model = generator(input_z, out_channel_dim, is_train=True)
    d_model_real, d_logits_real = discriminator(input_real, reuse=False)
    d_model_fake, d_logits_fake = discriminator(g_model, reuse=True)

    d_loss_real = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_real, labels=tf.ones_like(d_model_real)*0.9))
    d_loss_fake = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.zeros_like(d_model_fake)))
    g_loss = tf.reduce_mean(
        tf.nn.sigmoid_cross_entropy_with_logits(logits=d_logits_fake, labels=tf.ones_like(d_model_fake)))

    d_loss = d_loss_real + d_loss_fake
    
    return d_loss, g_loss


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_loss(model_loss)
Tests Passed

Optimization

Implement model_opt to create the optimization operations for the GANs. Use tf.trainable_variables to get all the trainable variables. Filter the variables with names that are in the discriminator and generator scope names. The function should return a tuple of (discriminator training operation, generator training operation).

In [9]:
def model_opt(d_loss, g_loss, learning_rate, beta1):
    """
    Get optimization operations
    :param d_loss: Discriminator loss Tensor
    :param g_loss: Generator loss Tensor
    :param learning_rate: Learning Rate Placeholder
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :return: A tuple of (discriminator training operation, generator training operation)
    """
    # TODO: Implement Function
    # Get weights and bias to update
    t_vars = tf.trainable_variables()
    d_vars = [var for var in t_vars if var.name.startswith('discriminator')]
    g_vars = [var for var in t_vars if var.name.startswith('generator')]

    # Optimize
    with tf.control_dependencies(tf.get_collection(tf.GraphKeys.UPDATE_OPS)):
        d_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(d_loss, var_list=d_vars)
        g_train_opt = tf.train.AdamOptimizer(learning_rate, beta1=beta1).minimize(g_loss, var_list=g_vars)
        
    return d_train_opt, g_train_opt


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
tests.test_model_opt(model_opt, tf)
Tests Passed

Neural Network Training

Show Output

Use this function to show the current output of the generator during training. It will help you determine how well the GANs is training.

In [10]:
"""
DON'T MODIFY ANYTHING IN THIS CELL
"""
import numpy as np

def show_generator_output(sess, n_images, input_z, out_channel_dim, image_mode):
    """
    Show example output for the generator
    :param sess: TensorFlow session
    :param n_images: Number of Images to display
    :param input_z: Input Z Tensor
    :param out_channel_dim: The number of channels in the output image
    :param image_mode: The mode to use for images ("RGB" or "L")
    """
    cmap = None if image_mode == 'RGB' else 'gray'
    z_dim = input_z.get_shape().as_list()[-1]
    example_z = np.random.uniform(-1, 1, size=[n_images, z_dim])

    samples = sess.run(
        generator(input_z, out_channel_dim, False),
        feed_dict={input_z: example_z})

    images_grid = helper.images_square_grid(samples, image_mode)
    pyplot.imshow(images_grid, cmap=cmap)
    pyplot.show()

Train

Implement train to build and train the GANs. Use the following functions you implemented:

  • model_inputs(image_width, image_height, image_channels, z_dim)
  • model_loss(input_real, input_z, out_channel_dim)
  • model_opt(d_loss, g_loss, learning_rate, beta1)

Use the show_generator_output to show generator output while you train. Running show_generator_output for every batch will drastically increase training time and increase the size of the notebook. It's recommended to print the generator output every 100 batches.

In [11]:
def train(epoch_count, batch_size, z_dim, learning_rate, beta1, get_batches, data_shape, data_image_mode):
    """
    Train the GAN
    :param epoch_count: Number of epochs
    :param batch_size: Batch Size
    :param z_dim: Z dimension
    :param learning_rate: Learning Rate
    :param beta1: The exponential decay rate for the 1st moment in the optimizer
    :param get_batches: Function to get batches
    :param data_shape: Shape of the data
    :param data_image_mode: The image mode to use for images ("RGB" or "L")
    """
    # TODO: Build Model
    
    input_real, input_z, lr = model_inputs(data_shape[1], data_shape[2], data_shape[3], z_dim)
    d_loss, g_loss = model_loss(input_real, input_z, data_shape[3])
    d_train_opt, g_train_opt = model_opt(d_loss, g_loss, learning_rate, beta1)
    
    t_vars = tf.trainable_variables()
    g_vars = [var for var in t_vars if var.name.startswith('generator')]
    saver = tf.train.Saver(var_list=g_vars)
    
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        for epoch_i in range(epoch_count):
            batches = 0
            for batch_images in get_batches(batch_size):
                # TODO: Train Model
                
                batch_z = np.random.uniform(-1, 1, size=(batch_size, z_dim))
                resc_batch_images = 2*batch_images ### rescaling to match tanh output 
                
                _ = sess.run(d_train_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                _ = sess.run(g_train_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                _ = sess.run(g_train_opt, feed_dict={input_real: batch_images, input_z: batch_z, lr: learning_rate})
                ### two times for generator to keep up with discriminator
                batches +=1
                
                if batches % 20 == 0:
                    # At the end of each epoch, get the losses and print them out
                    train_loss_d = d_loss.eval({input_real: batch_images, input_z: batch_z})
                    train_loss_g = g_loss.eval({input_real: batch_images, input_z: batch_z})

                    print("Epoch {}/{}...".format(epoch_i+1, epochs),
                          "Batch {}...".format(batches),
                          "Discriminator Loss: {:.4f}...".format(train_loss_d),
                          "Generator Loss: {:.4f}".format(train_loss_g))

                if batches % 200 == 0:
                    show_generator_output(sess, 25, input_z, data_shape[3], data_image_mode)
                
        show_generator_output(sess, 25, input_z, data_shape[3], data_image_mode)
        print('Finished!')
                

MNIST

Test your GANs architecture on MNIST. After 2 epochs, the GANs should be able to generate images that look like handwritten digits. Make sure the loss of the generator is lower than the loss of the discriminator or close to 0.

In [12]:
batch_size = 32
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 2

mnist_dataset = helper.Dataset('mnist', glob(os.path.join(data_dir, 'mnist/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, mnist_dataset.get_batches,
          mnist_dataset.shape, mnist_dataset.image_mode)
Epoch 1/2... Batch 20... Discriminator Loss: 4.6634... Generator Loss: 0.0143
Epoch 1/2... Batch 40... Discriminator Loss: 3.5872... Generator Loss: 0.0478
Epoch 1/2... Batch 60... Discriminator Loss: 2.5351... Generator Loss: 0.1482
Epoch 1/2... Batch 80... Discriminator Loss: 3.3224... Generator Loss: 0.0701
Epoch 1/2... Batch 100... Discriminator Loss: 2.5553... Generator Loss: 0.1883
Epoch 1/2... Batch 120... Discriminator Loss: 2.5684... Generator Loss: 0.1536
Epoch 1/2... Batch 140... Discriminator Loss: 2.6234... Generator Loss: 0.1491
Epoch 1/2... Batch 160... Discriminator Loss: 2.5528... Generator Loss: 0.1299
Epoch 1/2... Batch 180... Discriminator Loss: 2.9584... Generator Loss: 0.0840
Epoch 1/2... Batch 200... Discriminator Loss: 2.2105... Generator Loss: 0.1838
Epoch 1/2... Batch 220... Discriminator Loss: 2.0253... Generator Loss: 0.2988
Epoch 1/2... Batch 240... Discriminator Loss: 1.6188... Generator Loss: 0.4431
Epoch 1/2... Batch 260... Discriminator Loss: 2.0966... Generator Loss: 0.1967
Epoch 1/2... Batch 280... Discriminator Loss: 1.3069... Generator Loss: 0.5742
Epoch 1/2... Batch 300... Discriminator Loss: 2.0398... Generator Loss: 0.2075
Epoch 1/2... Batch 320... Discriminator Loss: 1.2061... Generator Loss: 0.6108
Epoch 1/2... Batch 340... Discriminator Loss: 1.9667... Generator Loss: 0.2531
Epoch 1/2... Batch 360... Discriminator Loss: 1.2516... Generator Loss: 0.5736
Epoch 1/2... Batch 380... Discriminator Loss: 1.4456... Generator Loss: 0.4455
Epoch 1/2... Batch 400... Discriminator Loss: 1.4016... Generator Loss: 0.4696
Epoch 1/2... Batch 420... Discriminator Loss: 1.8886... Generator Loss: 0.2566
Epoch 1/2... Batch 440... Discriminator Loss: 1.3754... Generator Loss: 0.4774
Epoch 1/2... Batch 460... Discriminator Loss: 2.0605... Generator Loss: 0.2190
Epoch 1/2... Batch 480... Discriminator Loss: 1.6931... Generator Loss: 0.3506
Epoch 1/2... Batch 500... Discriminator Loss: 1.0938... Generator Loss: 0.9154
Epoch 1/2... Batch 520... Discriminator Loss: 2.1337... Generator Loss: 0.1937
Epoch 1/2... Batch 540... Discriminator Loss: 0.8956... Generator Loss: 1.2436
Epoch 1/2... Batch 560... Discriminator Loss: 1.1872... Generator Loss: 0.6200
Epoch 1/2... Batch 580... Discriminator Loss: 1.2024... Generator Loss: 0.7026
Epoch 1/2... Batch 600... Discriminator Loss: 1.0852... Generator Loss: 0.7546
Epoch 1/2... Batch 620... Discriminator Loss: 0.9615... Generator Loss: 0.8019
Epoch 1/2... Batch 640... Discriminator Loss: 1.1969... Generator Loss: 0.5648
Epoch 1/2... Batch 660... Discriminator Loss: 1.7747... Generator Loss: 0.3094
Epoch 1/2... Batch 680... Discriminator Loss: 0.7420... Generator Loss: 1.2776
Epoch 1/2... Batch 700... Discriminator Loss: 0.5712... Generator Loss: 1.7497
Epoch 1/2... Batch 720... Discriminator Loss: 0.5714... Generator Loss: 1.8026
Epoch 1/2... Batch 740... Discriminator Loss: 0.5514... Generator Loss: 1.9882
Epoch 1/2... Batch 760... Discriminator Loss: 0.7470... Generator Loss: 1.5392
Epoch 1/2... Batch 780... Discriminator Loss: 0.5848... Generator Loss: 1.7680
Epoch 1/2... Batch 800... Discriminator Loss: 0.5814... Generator Loss: 1.8464
Epoch 1/2... Batch 820... Discriminator Loss: 0.6142... Generator Loss: 1.5705
Epoch 1/2... Batch 840... Discriminator Loss: 0.5230... Generator Loss: 2.4897
Epoch 1/2... Batch 860... Discriminator Loss: 0.6072... Generator Loss: 1.5772
Epoch 1/2... Batch 880... Discriminator Loss: 1.2841... Generator Loss: 0.5554
Epoch 1/2... Batch 900... Discriminator Loss: 0.8260... Generator Loss: 1.0489
Epoch 1/2... Batch 920... Discriminator Loss: 1.1235... Generator Loss: 0.7705
Epoch 1/2... Batch 940... Discriminator Loss: 1.1995... Generator Loss: 0.5965
Epoch 1/2... Batch 960... Discriminator Loss: 1.3474... Generator Loss: 0.5484
Epoch 1/2... Batch 980... Discriminator Loss: 0.5583... Generator Loss: 2.6732
Epoch 1/2... Batch 1000... Discriminator Loss: 1.4059... Generator Loss: 0.4592
Epoch 1/2... Batch 1020... Discriminator Loss: 1.4489... Generator Loss: 0.4337
Epoch 1/2... Batch 1040... Discriminator Loss: 1.1935... Generator Loss: 0.7115
Epoch 1/2... Batch 1060... Discriminator Loss: 1.0411... Generator Loss: 0.7610
Epoch 1/2... Batch 1080... Discriminator Loss: 1.5002... Generator Loss: 0.4174
Epoch 1/2... Batch 1100... Discriminator Loss: 0.7267... Generator Loss: 1.4320
Epoch 1/2... Batch 1120... Discriminator Loss: 0.9109... Generator Loss: 2.7626
Epoch 1/2... Batch 1140... Discriminator Loss: 0.8238... Generator Loss: 1.1607
Epoch 1/2... Batch 1160... Discriminator Loss: 0.8857... Generator Loss: 0.9359
Epoch 1/2... Batch 1180... Discriminator Loss: 0.8628... Generator Loss: 2.4531
Epoch 1/2... Batch 1200... Discriminator Loss: 0.4861... Generator Loss: 2.2386
Epoch 1/2... Batch 1220... Discriminator Loss: 0.5084... Generator Loss: 2.3747
Epoch 1/2... Batch 1240... Discriminator Loss: 0.8117... Generator Loss: 1.0257
Epoch 1/2... Batch 1260... Discriminator Loss: 1.1669... Generator Loss: 0.6280
Epoch 1/2... Batch 1280... Discriminator Loss: 1.2328... Generator Loss: 0.6474
Epoch 1/2... Batch 1300... Discriminator Loss: 1.2239... Generator Loss: 0.8709
Epoch 1/2... Batch 1320... Discriminator Loss: 1.4471... Generator Loss: 0.4359
Epoch 1/2... Batch 1340... Discriminator Loss: 0.5171... Generator Loss: 1.8862
Epoch 1/2... Batch 1360... Discriminator Loss: 1.0839... Generator Loss: 0.6902
Epoch 1/2... Batch 1380... Discriminator Loss: 1.2267... Generator Loss: 0.6502
Epoch 1/2... Batch 1400... Discriminator Loss: 0.6205... Generator Loss: 1.4622
Epoch 1/2... Batch 1420... Discriminator Loss: 0.5872... Generator Loss: 1.5692
Epoch 1/2... Batch 1440... Discriminator Loss: 1.3033... Generator Loss: 0.6040
Epoch 1/2... Batch 1460... Discriminator Loss: 1.5510... Generator Loss: 0.3934
Epoch 1/2... Batch 1480... Discriminator Loss: 1.1509... Generator Loss: 0.6161
Epoch 1/2... Batch 1500... Discriminator Loss: 1.2508... Generator Loss: 0.9646
Epoch 1/2... Batch 1520... Discriminator Loss: 0.5176... Generator Loss: 2.0426
Epoch 1/2... Batch 1540... Discriminator Loss: 0.9892... Generator Loss: 0.8900
Epoch 1/2... Batch 1560... Discriminator Loss: 1.2296... Generator Loss: 0.5549
Epoch 1/2... Batch 1580... Discriminator Loss: 0.3997... Generator Loss: 3.2192
Epoch 1/2... Batch 1600... Discriminator Loss: 0.4387... Generator Loss: 2.5972
Epoch 1/2... Batch 1620... Discriminator Loss: 0.4438... Generator Loss: 3.3519
Epoch 1/2... Batch 1640... Discriminator Loss: 0.4734... Generator Loss: 4.0006
Epoch 1/2... Batch 1660... Discriminator Loss: 0.6458... Generator Loss: 1.4715
Epoch 1/2... Batch 1680... Discriminator Loss: 0.4463... Generator Loss: 3.9365
Epoch 1/2... Batch 1700... Discriminator Loss: 1.3770... Generator Loss: 0.5071
Epoch 1/2... Batch 1720... Discriminator Loss: 2.1410... Generator Loss: 0.2052
Epoch 1/2... Batch 1740... Discriminator Loss: 0.4174... Generator Loss: 3.0480
Epoch 1/2... Batch 1760... Discriminator Loss: 0.6597... Generator Loss: 1.6500
Epoch 1/2... Batch 1780... Discriminator Loss: 0.7241... Generator Loss: 1.3556
Epoch 1/2... Batch 1800... Discriminator Loss: 2.0427... Generator Loss: 0.2385
Epoch 1/2... Batch 1820... Discriminator Loss: 1.2507... Generator Loss: 0.5970
Epoch 1/2... Batch 1840... Discriminator Loss: 0.9404... Generator Loss: 0.8781
Epoch 1/2... Batch 1860... Discriminator Loss: 0.4580... Generator Loss: 2.5759
Epoch 2/2... Batch 20... Discriminator Loss: 0.8358... Generator Loss: 1.0394
Epoch 2/2... Batch 40... Discriminator Loss: 0.7086... Generator Loss: 1.3098
Epoch 2/2... Batch 60... Discriminator Loss: 0.7981... Generator Loss: 1.2265
Epoch 2/2... Batch 80... Discriminator Loss: 0.9745... Generator Loss: 0.9013
Epoch 2/2... Batch 100... Discriminator Loss: 1.0190... Generator Loss: 0.8031
Epoch 2/2... Batch 120... Discriminator Loss: 0.8207... Generator Loss: 1.0205
Epoch 2/2... Batch 140... Discriminator Loss: 1.6264... Generator Loss: 0.4119
Epoch 2/2... Batch 160... Discriminator Loss: 0.8596... Generator Loss: 0.9696
Epoch 2/2... Batch 180... Discriminator Loss: 1.3535... Generator Loss: 0.6050
Epoch 2/2... Batch 200... Discriminator Loss: 0.5962... Generator Loss: 2.3227
Epoch 2/2... Batch 220... Discriminator Loss: 0.7382... Generator Loss: 1.4144
Epoch 2/2... Batch 240... Discriminator Loss: 0.8974... Generator Loss: 1.0010
Epoch 2/2... Batch 260... Discriminator Loss: 0.9078... Generator Loss: 1.7661
Epoch 2/2... Batch 280... Discriminator Loss: 0.7343... Generator Loss: 1.1550
Epoch 2/2... Batch 300... Discriminator Loss: 1.1113... Generator Loss: 0.6878
Epoch 2/2... Batch 320... Discriminator Loss: 0.9428... Generator Loss: 2.5305
Epoch 2/2... Batch 340... Discriminator Loss: 1.0418... Generator Loss: 0.7332
Epoch 2/2... Batch 360... Discriminator Loss: 0.9135... Generator Loss: 0.8531
Epoch 2/2... Batch 380... Discriminator Loss: 0.4857... Generator Loss: 2.1525
Epoch 2/2... Batch 400... Discriminator Loss: 0.4094... Generator Loss: 2.9471
Epoch 2/2... Batch 420... Discriminator Loss: 0.7970... Generator Loss: 1.6122
Epoch 2/2... Batch 440... Discriminator Loss: 0.5645... Generator Loss: 1.9164
Epoch 2/2... Batch 460... Discriminator Loss: 1.1227... Generator Loss: 0.7164
Epoch 2/2... Batch 480... Discriminator Loss: 1.0999... Generator Loss: 0.7741
Epoch 2/2... Batch 500... Discriminator Loss: 1.0048... Generator Loss: 0.9900
Epoch 2/2... Batch 520... Discriminator Loss: 1.0469... Generator Loss: 0.7480
Epoch 2/2... Batch 540... Discriminator Loss: 0.4705... Generator Loss: 2.6681
Epoch 2/2... Batch 560... Discriminator Loss: 1.0513... Generator Loss: 0.7196
Epoch 2/2... Batch 580... Discriminator Loss: 0.8390... Generator Loss: 1.1273
Epoch 2/2... Batch 600... Discriminator Loss: 0.5522... Generator Loss: 1.7831
Epoch 2/2... Batch 620... Discriminator Loss: 0.3839... Generator Loss: 3.3011
Epoch 2/2... Batch 640... Discriminator Loss: 0.7912... Generator Loss: 1.0759
Epoch 2/2... Batch 660... Discriminator Loss: 0.8487... Generator Loss: 1.0173
Epoch 2/2... Batch 680... Discriminator Loss: 2.2134... Generator Loss: 0.2238
Epoch 2/2... Batch 700... Discriminator Loss: 0.6918... Generator Loss: 1.4926
Epoch 2/2... Batch 720... Discriminator Loss: 0.6279... Generator Loss: 1.5151
Epoch 2/2... Batch 740... Discriminator Loss: 0.5358... Generator Loss: 2.1226
Epoch 2/2... Batch 760... Discriminator Loss: 0.3938... Generator Loss: 4.2326
Epoch 2/2... Batch 780... Discriminator Loss: 0.7886... Generator Loss: 1.3648
Epoch 2/2... Batch 800... Discriminator Loss: 1.0037... Generator Loss: 0.8110
Epoch 2/2... Batch 820... Discriminator Loss: 0.6984... Generator Loss: 1.3174
Epoch 2/2... Batch 840... Discriminator Loss: 1.3891... Generator Loss: 0.5197
Epoch 2/2... Batch 860... Discriminator Loss: 0.9075... Generator Loss: 0.9598
Epoch 2/2... Batch 880... Discriminator Loss: 1.1688... Generator Loss: 0.5824
Epoch 2/2... Batch 900... Discriminator Loss: 0.3541... Generator Loss: 4.1293
Epoch 2/2... Batch 920... Discriminator Loss: 2.2591... Generator Loss: 0.1931
Epoch 2/2... Batch 940... Discriminator Loss: 0.9563... Generator Loss: 0.8882
Epoch 2/2... Batch 960... Discriminator Loss: 1.5533... Generator Loss: 0.3936
Epoch 2/2... Batch 980... Discriminator Loss: 1.0125... Generator Loss: 0.9082
Epoch 2/2... Batch 1000... Discriminator Loss: 1.1236... Generator Loss: 0.7490
Epoch 2/2... Batch 1020... Discriminator Loss: 1.1162... Generator Loss: 0.6794
Epoch 2/2... Batch 1040... Discriminator Loss: 0.4056... Generator Loss: 3.1729
Epoch 2/2... Batch 1060... Discriminator Loss: 0.6841... Generator Loss: 1.4102
Epoch 2/2... Batch 1080... Discriminator Loss: 0.9864... Generator Loss: 0.8696
Epoch 2/2... Batch 1100... Discriminator Loss: 0.5379... Generator Loss: 1.9445
Epoch 2/2... Batch 1120... Discriminator Loss: 0.5590... Generator Loss: 1.7485
Epoch 2/2... Batch 1140... Discriminator Loss: 0.7618... Generator Loss: 1.1891
Epoch 2/2... Batch 1160... Discriminator Loss: 0.7060... Generator Loss: 1.2444
Epoch 2/2... Batch 1180... Discriminator Loss: 1.2138... Generator Loss: 0.6324
Epoch 2/2... Batch 1200... Discriminator Loss: 0.6965... Generator Loss: 1.3339
Epoch 2/2... Batch 1220... Discriminator Loss: 0.8129... Generator Loss: 1.1240
Epoch 2/2... Batch 1240... Discriminator Loss: 0.7802... Generator Loss: 1.6926
Epoch 2/2... Batch 1260... Discriminator Loss: 0.4875... Generator Loss: 2.4147
Epoch 2/2... Batch 1280... Discriminator Loss: 0.9558... Generator Loss: 0.8615
Epoch 2/2... Batch 1300... Discriminator Loss: 0.9500... Generator Loss: 0.9591
Epoch 2/2... Batch 1320... Discriminator Loss: 0.7229... Generator Loss: 2.2851
Epoch 2/2... Batch 1340... Discriminator Loss: 1.4846... Generator Loss: 0.4328
Epoch 2/2... Batch 1360... Discriminator Loss: 1.2582... Generator Loss: 0.5903
Epoch 2/2... Batch 1380... Discriminator Loss: 0.8140... Generator Loss: 1.2592
Epoch 2/2... Batch 1400... Discriminator Loss: 1.8142... Generator Loss: 0.3331
Epoch 2/2... Batch 1420... Discriminator Loss: 1.1806... Generator Loss: 0.6673
Epoch 2/2... Batch 1440... Discriminator Loss: 1.2693... Generator Loss: 0.5721
Epoch 2/2... Batch 1460... Discriminator Loss: 1.1781... Generator Loss: 0.7130
Epoch 2/2... Batch 1480... Discriminator Loss: 1.1618... Generator Loss: 0.7550
Epoch 2/2... Batch 1500... Discriminator Loss: 0.6848... Generator Loss: 1.5996
Epoch 2/2... Batch 1520... Discriminator Loss: 1.0241... Generator Loss: 0.7618
Epoch 2/2... Batch 1540... Discriminator Loss: 1.1002... Generator Loss: 0.7011
Epoch 2/2... Batch 1560... Discriminator Loss: 1.1699... Generator Loss: 0.6205
Epoch 2/2... Batch 1580... Discriminator Loss: 1.0587... Generator Loss: 0.8578
Epoch 2/2... Batch 1600... Discriminator Loss: 0.8424... Generator Loss: 1.0847
Epoch 2/2... Batch 1620... Discriminator Loss: 0.5756... Generator Loss: 1.6809
Epoch 2/2... Batch 1640... Discriminator Loss: 0.6744... Generator Loss: 1.4069
Epoch 2/2... Batch 1660... Discriminator Loss: 0.5429... Generator Loss: 2.0314
Epoch 2/2... Batch 1680... Discriminator Loss: 0.6505... Generator Loss: 1.5552
Epoch 2/2... Batch 1700... Discriminator Loss: 1.4352... Generator Loss: 0.4816
Epoch 2/2... Batch 1720... Discriminator Loss: 0.9557... Generator Loss: 0.9058
Epoch 2/2... Batch 1740... Discriminator Loss: 0.7821... Generator Loss: 1.1602
Epoch 2/2... Batch 1760... Discriminator Loss: 1.1372... Generator Loss: 0.6524
Epoch 2/2... Batch 1780... Discriminator Loss: 1.1836... Generator Loss: 0.6841
Epoch 2/2... Batch 1800... Discriminator Loss: 1.3819... Generator Loss: 0.4909
Epoch 2/2... Batch 1820... Discriminator Loss: 2.0389... Generator Loss: 0.2294
Epoch 2/2... Batch 1840... Discriminator Loss: 0.6952... Generator Loss: 1.4329
Epoch 2/2... Batch 1860... Discriminator Loss: 0.9362... Generator Loss: 0.9945
Finished!

CelebA

Run your GANs on CelebA. It will take around 20 minutes on the average GPU to run one epoch. You can run the whole epoch or stop when it starts to generate realistic faces.

In [13]:
batch_size = 32
z_dim = 100
learning_rate = 0.0002
beta1 = 0.5


"""
DON'T MODIFY ANYTHING IN THIS CELL THAT IS BELOW THIS LINE
"""
epochs = 1

celeba_dataset = helper.Dataset('celeba', glob(os.path.join(data_dir, 'img_align_celeba/*.jpg')))
with tf.Graph().as_default():
    train(epochs, batch_size, z_dim, learning_rate, beta1, celeba_dataset.get_batches,
          celeba_dataset.shape, celeba_dataset.image_mode)
Epoch 1/1... Batch 20... Discriminator Loss: 5.5332... Generator Loss: 0.0060
Epoch 1/1... Batch 40... Discriminator Loss: 4.5747... Generator Loss: 0.0155
Epoch 1/1... Batch 60... Discriminator Loss: 2.0079... Generator Loss: 0.2530
Epoch 1/1... Batch 80... Discriminator Loss: 1.4379... Generator Loss: 0.5871
Epoch 1/1... Batch 100... Discriminator Loss: 1.7800... Generator Loss: 0.3296
Epoch 1/1... Batch 120... Discriminator Loss: 1.0551... Generator Loss: 0.7503
Epoch 1/1... Batch 140... Discriminator Loss: 1.4300... Generator Loss: 0.4765
Epoch 1/1... Batch 160... Discriminator Loss: 1.8912... Generator Loss: 0.2878
Epoch 1/1... Batch 180... Discriminator Loss: 1.4644... Generator Loss: 0.6227
Epoch 1/1... Batch 200... Discriminator Loss: 2.3873... Generator Loss: 0.1767
Epoch 1/1... Batch 220... Discriminator Loss: 1.4400... Generator Loss: 0.8695
Epoch 1/1... Batch 240... Discriminator Loss: 1.8968... Generator Loss: 0.5373
Epoch 1/1... Batch 260... Discriminator Loss: 1.3233... Generator Loss: 0.8188
Epoch 1/1... Batch 280... Discriminator Loss: 1.8663... Generator Loss: 0.4863
Epoch 1/1... Batch 300... Discriminator Loss: 1.5417... Generator Loss: 0.5745
Epoch 1/1... Batch 320... Discriminator Loss: 1.4463... Generator Loss: 0.6613
Epoch 1/1... Batch 340... Discriminator Loss: 1.1638... Generator Loss: 0.8145
Epoch 1/1... Batch 360... Discriminator Loss: 1.4793... Generator Loss: 0.6187
Epoch 1/1... Batch 380... Discriminator Loss: 1.3643... Generator Loss: 0.7550
Epoch 1/1... Batch 400... Discriminator Loss: 1.5787... Generator Loss: 0.5448
Epoch 1/1... Batch 420... Discriminator Loss: 1.7366... Generator Loss: 0.5860
Epoch 1/1... Batch 440... Discriminator Loss: 1.4423... Generator Loss: 0.6795
Epoch 1/1... Batch 460... Discriminator Loss: 1.7336... Generator Loss: 0.4786
Epoch 1/1... Batch 480... Discriminator Loss: 1.5958... Generator Loss: 0.5154
Epoch 1/1... Batch 500... Discriminator Loss: 1.5685... Generator Loss: 0.5275
Epoch 1/1... Batch 520... Discriminator Loss: 1.4846... Generator Loss: 0.6420
Epoch 1/1... Batch 540... Discriminator Loss: 1.2622... Generator Loss: 0.7938
Epoch 1/1... Batch 560... Discriminator Loss: 1.6901... Generator Loss: 0.4841
Epoch 1/1... Batch 580... Discriminator Loss: 1.6810... Generator Loss: 0.6084
Epoch 1/1... Batch 600... Discriminator Loss: 1.5129... Generator Loss: 0.6497
Epoch 1/1... Batch 620... Discriminator Loss: 1.4675... Generator Loss: 0.6359
Epoch 1/1... Batch 640... Discriminator Loss: 1.6401... Generator Loss: 0.5427
Epoch 1/1... Batch 660... Discriminator Loss: 1.4262... Generator Loss: 0.6096
Epoch 1/1... Batch 680... Discriminator Loss: 1.5046... Generator Loss: 0.5969
Epoch 1/1... Batch 700... Discriminator Loss: 1.4907... Generator Loss: 0.6647
Epoch 1/1... Batch 720... Discriminator Loss: 1.8119... Generator Loss: 0.4483
Epoch 1/1... Batch 740... Discriminator Loss: 1.4562... Generator Loss: 0.5587
Epoch 1/1... Batch 760... Discriminator Loss: 1.5915... Generator Loss: 0.5976
Epoch 1/1... Batch 780... Discriminator Loss: 1.5324... Generator Loss: 0.5431
Epoch 1/1... Batch 800... Discriminator Loss: 1.6560... Generator Loss: 0.5359
Epoch 1/1... Batch 820... Discriminator Loss: 1.5876... Generator Loss: 0.4902
Epoch 1/1... Batch 840... Discriminator Loss: 1.6741... Generator Loss: 0.4696
Epoch 1/1... Batch 860... Discriminator Loss: 1.4025... Generator Loss: 0.7291
Epoch 1/1... Batch 880... Discriminator Loss: 1.1341... Generator Loss: 0.7324
Epoch 1/1... Batch 900... Discriminator Loss: 1.4507... Generator Loss: 0.6056
Epoch 1/1... Batch 920... Discriminator Loss: 1.4664... Generator Loss: 0.4537
Epoch 1/1... Batch 940... Discriminator Loss: 1.6563... Generator Loss: 0.3610
Epoch 1/1... Batch 960... Discriminator Loss: 1.3275... Generator Loss: 0.7700
Epoch 1/1... Batch 980... Discriminator Loss: 2.0320... Generator Loss: 0.2362
Epoch 1/1... Batch 1000... Discriminator Loss: 1.3383... Generator Loss: 0.7427
Epoch 1/1... Batch 1020... Discriminator Loss: 1.5729... Generator Loss: 0.4457
Epoch 1/1... Batch 1040... Discriminator Loss: 0.9263... Generator Loss: 1.1136
Epoch 1/1... Batch 1060... Discriminator Loss: 1.0138... Generator Loss: 1.3204
Epoch 1/1... Batch 1080... Discriminator Loss: 2.2783... Generator Loss: 0.2164
Epoch 1/1... Batch 1100... Discriminator Loss: 0.8986... Generator Loss: 2.4583
Epoch 1/1... Batch 1120... Discriminator Loss: 1.9051... Generator Loss: 0.2934
Epoch 1/1... Batch 1140... Discriminator Loss: 2.1786... Generator Loss: 0.2079
Epoch 1/1... Batch 1160... Discriminator Loss: 1.6089... Generator Loss: 0.3968
Epoch 1/1... Batch 1180... Discriminator Loss: 1.7605... Generator Loss: 0.3602
Epoch 1/1... Batch 1200... Discriminator Loss: 0.8183... Generator Loss: 1.8254
Epoch 1/1... Batch 1220... Discriminator Loss: 0.7578... Generator Loss: 2.1349
Epoch 1/1... Batch 1240... Discriminator Loss: 0.8341... Generator Loss: 2.0940
Epoch 1/1... Batch 1260... Discriminator Loss: 1.9338... Generator Loss: 0.2877
Epoch 1/1... Batch 1280... Discriminator Loss: 1.8927... Generator Loss: 0.3156
Epoch 1/1... Batch 1300... Discriminator Loss: 1.9326... Generator Loss: 0.2515
Epoch 1/1... Batch 1320... Discriminator Loss: 1.3682... Generator Loss: 0.6033
Epoch 1/1... Batch 1340... Discriminator Loss: 0.9127... Generator Loss: 1.4203
Epoch 1/1... Batch 1360... Discriminator Loss: 1.4873... Generator Loss: 0.5472
Epoch 1/1... Batch 1380... Discriminator Loss: 1.6987... Generator Loss: 0.5602
Epoch 1/1... Batch 1400... Discriminator Loss: 1.6847... Generator Loss: 0.3729
Epoch 1/1... Batch 1420... Discriminator Loss: 1.5055... Generator Loss: 0.5676
Epoch 1/1... Batch 1440... Discriminator Loss: 1.1329... Generator Loss: 1.1311
Epoch 1/1... Batch 1460... Discriminator Loss: 2.0347... Generator Loss: 0.2285
Epoch 1/1... Batch 1480... Discriminator Loss: 1.5979... Generator Loss: 0.6137
Epoch 1/1... Batch 1500... Discriminator Loss: 1.3762... Generator Loss: 0.6367
Epoch 1/1... Batch 1520... Discriminator Loss: 1.7691... Generator Loss: 0.3337
Epoch 1/1... Batch 1540... Discriminator Loss: 1.9456... Generator Loss: 0.3507
Epoch 1/1... Batch 1560... Discriminator Loss: 1.5888... Generator Loss: 0.5207
Epoch 1/1... Batch 1580... Discriminator Loss: 1.6796... Generator Loss: 0.4820
Epoch 1/1... Batch 1600... Discriminator Loss: 1.6711... Generator Loss: 0.4697
Epoch 1/1... Batch 1620... Discriminator Loss: 1.5231... Generator Loss: 0.4961
Epoch 1/1... Batch 1640... Discriminator Loss: 1.9671... Generator Loss: 0.2767
Epoch 1/1... Batch 1660... Discriminator Loss: 1.8223... Generator Loss: 0.3964
Epoch 1/1... Batch 1680... Discriminator Loss: 1.6371... Generator Loss: 0.3691
Epoch 1/1... Batch 1700... Discriminator Loss: 1.9372... Generator Loss: 0.2978
Epoch 1/1... Batch 1720... Discriminator Loss: 1.6608... Generator Loss: 0.3939
Epoch 1/1... Batch 1740... Discriminator Loss: 0.7858... Generator Loss: 3.0720
Epoch 1/1... Batch 1760... Discriminator Loss: 0.9538... Generator Loss: 1.2221
Epoch 1/1... Batch 1780... Discriminator Loss: 1.2871... Generator Loss: 0.5345
Epoch 1/1... Batch 1800... Discriminator Loss: 1.9619... Generator Loss: 0.3665
Epoch 1/1... Batch 1820... Discriminator Loss: 1.7052... Generator Loss: 0.4180
Epoch 1/1... Batch 1840... Discriminator Loss: 1.7523... Generator Loss: 0.5478
Epoch 1/1... Batch 1860... Discriminator Loss: 1.5179... Generator Loss: 0.5306
Epoch 1/1... Batch 1880... Discriminator Loss: 1.7274... Generator Loss: 0.3775
Epoch 1/1... Batch 1900... Discriminator Loss: 1.8358... Generator Loss: 0.3409
Epoch 1/1... Batch 1920... Discriminator Loss: 1.3475... Generator Loss: 0.6212
Epoch 1/1... Batch 1940... Discriminator Loss: 1.5441... Generator Loss: 0.4829
Epoch 1/1... Batch 1960... Discriminator Loss: 1.6484... Generator Loss: 0.4412
Epoch 1/1... Batch 1980... Discriminator Loss: 1.3034... Generator Loss: 0.7104
Epoch 1/1... Batch 2000... Discriminator Loss: 2.1445... Generator Loss: 0.2071
Epoch 1/1... Batch 2020... Discriminator Loss: 1.4340... Generator Loss: 0.6204
Epoch 1/1... Batch 2040... Discriminator Loss: 1.7516... Generator Loss: 0.3695
Epoch 1/1... Batch 2060... Discriminator Loss: 1.6733... Generator Loss: 0.3591
Epoch 1/1... Batch 2080... Discriminator Loss: 1.4393... Generator Loss: 0.6106
Epoch 1/1... Batch 2100... Discriminator Loss: 2.3620... Generator Loss: 0.1603
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Epoch 1/1... Batch 5220... Discriminator Loss: 2.0328... Generator Loss: 0.2740
Epoch 1/1... Batch 5240... Discriminator Loss: 1.9779... Generator Loss: 0.3141
Epoch 1/1... Batch 5260... Discriminator Loss: 2.0509... Generator Loss: 0.2514
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Epoch 1/1... Batch 5400... Discriminator Loss: 1.9763... Generator Loss: 0.2771
Epoch 1/1... Batch 5420... Discriminator Loss: 1.6864... Generator Loss: 0.5099
Epoch 1/1... Batch 5440... Discriminator Loss: 1.4333... Generator Loss: 0.4675
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Epoch 1/1... Batch 5700... Discriminator Loss: 2.3569... Generator Loss: 0.1959
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Epoch 1/1... Batch 5740... Discriminator Loss: 1.6523... Generator Loss: 0.4142
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Epoch 1/1... Batch 5780... Discriminator Loss: 1.5027... Generator Loss: 0.4509
Epoch 1/1... Batch 5800... Discriminator Loss: 1.7450... Generator Loss: 0.5716
Epoch 1/1... Batch 5820... Discriminator Loss: 2.0089... Generator Loss: 0.2340
Epoch 1/1... Batch 5840... Discriminator Loss: 1.5377... Generator Loss: 0.4571
Epoch 1/1... Batch 5860... Discriminator Loss: 2.2660... Generator Loss: 0.1958
Epoch 1/1... Batch 5880... Discriminator Loss: 1.6480... Generator Loss: 0.3835
Epoch 1/1... Batch 5900... Discriminator Loss: 1.8733... Generator Loss: 0.2735
Epoch 1/1... Batch 5920... Discriminator Loss: 1.8916... Generator Loss: 0.3106
Epoch 1/1... Batch 5940... Discriminator Loss: 1.9007... Generator Loss: 0.4536
Epoch 1/1... Batch 5960... Discriminator Loss: 2.3723... Generator Loss: 0.1721
Epoch 1/1... Batch 5980... Discriminator Loss: 1.7256... Generator Loss: 0.3484
Epoch 1/1... Batch 6000... Discriminator Loss: 1.6657... Generator Loss: 0.4714
Epoch 1/1... Batch 6020... Discriminator Loss: 2.0472... Generator Loss: 0.2180
Epoch 1/1... Batch 6040... Discriminator Loss: 1.2677... Generator Loss: 0.6375
Epoch 1/1... Batch 6060... Discriminator Loss: 1.8247... Generator Loss: 0.2961
Epoch 1/1... Batch 6080... Discriminator Loss: 1.8926... Generator Loss: 0.2567
Epoch 1/1... Batch 6100... Discriminator Loss: 1.8705... Generator Loss: 0.2806
Epoch 1/1... Batch 6120... Discriminator Loss: 2.1900... Generator Loss: 0.1876
Epoch 1/1... Batch 6140... Discriminator Loss: 1.5808... Generator Loss: 0.4262
Epoch 1/1... Batch 6160... Discriminator Loss: 1.9876... Generator Loss: 0.2704
Epoch 1/1... Batch 6180... Discriminator Loss: 1.7558... Generator Loss: 0.3201
Epoch 1/1... Batch 6200... Discriminator Loss: 1.7110... Generator Loss: 0.3588
Epoch 1/1... Batch 6220... Discriminator Loss: 1.7527... Generator Loss: 0.3062
Epoch 1/1... Batch 6240... Discriminator Loss: 1.7609... Generator Loss: 0.3580
Epoch 1/1... Batch 6260... Discriminator Loss: 1.7411... Generator Loss: 0.3712
Epoch 1/1... Batch 6280... Discriminator Loss: 1.6188... Generator Loss: 0.3767
Epoch 1/1... Batch 6300... Discriminator Loss: 2.0414... Generator Loss: 0.2610
Epoch 1/1... Batch 6320... Discriminator Loss: 1.9419... Generator Loss: 0.2771
Finished!

Submitting This Project

When submitting this project, make sure to run all the cells before saving the notebook. Save the notebook file as "dlnd_face_generation.ipynb" and save it as a HTML file under "File" -> "Download as". Include the "helper.py" and "problem_unittests.py" files in your submission.